Developing an Evolutionary Neural Network Model for Stock Index Forecasting

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing methods for improving accuracy of the proposed model. We test capability of the proposed method by applying it to forecast Tehran Stock Exchange Prices Indexes (TEPIX) which is used in literature, and compare the results with previous forecasting methods and Back-propagation neural network (BPNN). Results show that the proposed approach is able to cope with the fluctuation of stock market values and it also yields good forecasting accuracy. So it can be considered as a suitable tool to deal with stock market forecasting problems.